Top Hybrid Machine Learning Engineer Jobs

Design and conduct advanced data analyses and develop machine learning models for mental health research, working with large datasets and multimodal methods.

Lead design, development, and monitoring of production ML and LLM/NLP systems. Own feature engineering, evaluation frameworks, model diagnostics, bias analysis, and cross-functional productionization with Product and Engineering.

Work on prototyping and implementing audio ML algorithms for real-time applications. Present findings and integrate solutions into existing platforms. Collaborate with experts in ML and DSP.

The Senior Machine Learning Engineer will design, implement, and optimize machine learning models, drive AI initiatives, and collaborate cross-functionally to enhance SailPoint's AI capabilities.

Lead the architecture and implementation of a scalable AI/ML platform, focusing on data pipelines and integration of ML capabilities in business systems. Mentor and collaborate with cross-functional teams to optimize performance and deliver business value.

The Staff Machine Learning Engineer will lead the development of AI solutions, ensure project delivery, and manage technical teams while interfacing with customers. Responsibilities include designing data pipelines, overseeing machine learning model development, and facilitating customer engagement.

The Machine Learning Engineer will design, implement, and manage complex ML algorithms, focusing on computer vision and data analysis while collaborating with team members to transition algorithms to production.

The Senior Machine Learning Engineer will develop and enhance Moveworks' NLU and agentic AI capabilities, deliver production-quality code, and solve complex engineering challenges in conversational AI.

Lead the development of ML models for search and personalization at Realtor.com, driving innovation and mentoring talent in a collaborative environment.

The Staff Applied AI/ML Engineer will develop, test, and deploy AI/ML models, transition models to production, and collaborate with teams to align solutions with business needs.